


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard




def  a   (lowerCAmelCase__   ):
   assert "mock" in _fsspec_registry
   assert "bz2" in _fsspec_registry




def  a   ():
   assert "mock" not in _fsspec_registry
   assert "bz2" in _fsspec_registry




def  a   ():
   __a					=  """mock-s3-bucket"""
   __a					=  f'''s3://{mock_bucket}'''
   __a					=  extract_path_from_uri(lowerCAmelCase__   )
   assert dataset_path.startswith("""s3://"""   ) is False

   __a					=  """./local/path"""
   __a					=  extract_path_from_uri(lowerCAmelCase__   )
   assert dataset_path == new_dataset_path




def  a   (lowerCAmelCase__   ):
   __a					=  is_remote_filesystem(lowerCAmelCase__   )
   assert is_remote is True

   __a					=  fsspec.filesystem("""file"""   )

   __a					=  is_remote_filesystem(lowerCAmelCase__   )
   assert is_remote is False




@pytest.mark.parametrize("""compression_fs_class"""		,				lowerCAmelCase__   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
   __a					=  input_paths[compression_fs_class.protocol]
   if input_path is None:
      __a					=  f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
      if compression_fs_class.protocol == "lz4":
         reason += require_lza.kwargs["reason"]
      elif compression_fs_class.protocol == "zstd":
         reason += require_zstandard.kwargs["reason"]
      pytest.skip(lowerCAmelCase__   )
   __a					=  fsspec.filesystem(compression_fs_class.protocol		,				fo=lowerCAmelCase__   )
   assert isinstance(lowerCAmelCase__		,				lowerCAmelCase__   )
   __a					=  os.path.basename(lowerCAmelCase__   )
   __a					=  expected_filename[: expected_filename.rindex("""."""   )]
   assert fs.glob("""*"""   ) == [expected_filename]
   with fs.open(lowerCAmelCase__		,				"""r"""		,				encoding="""utf-8"""   ) as f, open(lowerCAmelCase__		,				encoding="""utf-8"""   ) as expected_file:
      assert f.read() == expected_file.read()




@pytest.mark.parametrize("""protocol"""		,				["""zip""", """gzip"""]   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  {"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
   __a					=  compressed_file_paths[protocol]
   __a					=  """dataset.jsonl"""
   __a					=  f'''{protocol}://{member_file_path}::{compressed_file_path}'''
   __a	,    *__a					=  fsspec.get_fs_token_paths(lowerCAmelCase__   )
   assert fs.isfile(lowerCAmelCase__   )
   assert not fs.isfile("""non_existing_""" + member_file_path   )




@pytest.mark.integration
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  hf_api.dataset_info(lowerCAmelCase__		,				token=lowerCAmelCase__   )
   __a					=  HfFileSystem(repo_info=lowerCAmelCase__		,				token=lowerCAmelCase__   )
   assert sorted(hffs.glob("""*"""   )   ) == [".gitattributes", "data"]
   assert hffs.isdir("""data"""   )
   assert hffs.isfile(""".gitattributes"""   ) and hffs.isfile("""data/text_data.txt"""   )
   with open(lowerCAmelCase__   ) as f:
      assert hffs.open("""data/text_data.txt"""		,				"""r"""   ).read() == f.read()




def  a   ():
   __a					=  """bz2"""

   # Import module
   import datasets.filesystems

   # Overwrite protocol and reload
   register_implementation(lowerCAmelCase__		,				lowerCAmelCase__		,				clobber=lowerCAmelCase__   )
   with pytest.warns(lowerCAmelCase__   ) as warning_info:
      importlib.reload(datasets.filesystems   )

   assert len(lowerCAmelCase__   ) == 1
   assert (
       str(warning_info[0].message   )
       == f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
   )






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


